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This paper aims to examine the evolution of a competitive market structure over time through the lens of competitive group membership dynamics.

A new hidden Markov modeling approach is devised that accounts for the three sources of competitive heterogeneity involving managerial strategy, corporate performance and the impact of strategy on performance. In addition, some observed “entry” and “exit” states are considered to model firms’ entry into and exit from the market. The proposed model is illustrated with an investigation of the US banking industry based on a data set created from the COMPUSTAT database. This paper estimated the model within the Bayesian framework and devised a reversible jump Markov chain Monte Carlo estimation procedure to determine the number of latent competitive groups and uncover the characteristics of each group.

This paper shows that the US banking industry, contrary to the prior findings of having a relatively stable structure, has, in fact, gone through dramatic changes in the past number of decades.

Contrary to prior work that has primarily focused on managerial strategy to study market evolutions, the competitive groups perspective accounts for all three sources of intra-industry competitive heterogeneity. In addition, unlike prior research, the analysis is not limited to firms remaining in the panel of study for the entire observation period. Such limitation results in missing the various changes that occur in the competitive market structure because of the new entrants or the struggling firms that do not survive in the market.

Customer value has recently become a primary focus among many strategy researchers and practitioners as an essential element of a firm's competitive strategy. Many firms are engaged in some form of customer value analysis (CVA), which involves a structural analysis of the antecedent factors of perceived value (i.e. perceived quality and perceived price) to assess their relative importance in the perceptions of their buyers. Previous CVA research has focused upon using aggregate market or market segment level analyses. The purpose of this paper is to expose the limitations of implementing CVA on either an aggregate or market segment level basis, and propose an alternative individual level approach.

The paper develops an extended hierarchical Bayesian approach for cross‐sectional data with one observation per response unit, which allows for estimation at the individual firm level to make CVA more useful. This paper demonstrates the utility of the proposed Bayesian methodology involving a CVA study conducted for a large electric utility company. It also compares the empirical results from aggregate, market segment, and the proposed individual level analyses, and show how traditional approaches mask underlying price and quality importance.

Marketing and management strategy researchers need to exhibit care when conducting such CVA analyses as underlying heterogeneity can be masked when aggregate market or segment level analyses are conducted.

This paper provides a new hierarchical Bayes recursive simultaneous model formulation for CVA analyses to provide individual level insights with cross‐sectional data.

This chapter examines the conundrum of juvenile immigration law and policy and argues that it is a present-day manifestation of “child-saving” in rhetoric, disposition, and human capital harm. In support of this thesis, the chapter reviews the pertinent human rights, law, and social science evidence, and it concludes that the maintenance of the nation’s existing immigration policy only makes sense within the context of the intentions of the 19th century child-saving movement. To substantiate this view, the political-economic drivers of contemporary US immigration policy (i.e., its child-saving dynamics) are explored. The chapter concludes by speculatively addressing the character (i.e., the form and quality) of modern-day juvenile immigration policy as child-saving informed by the philosophy and criticism of Psychological Jurisprudence (PJ).

The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.

The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.

The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.

The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.

The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.

This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.

In the present paper we explore the long-term influence of childhood neglect on violent behavior in the transition to adulthood. In particular, we test whether neglect is spuriously related to violence due to their common association with academic achievement, physical abuse, and general offending. We then ask whether neglect has an indirect effect on violence through its impact on parental attachment, alcohol use, emotional negativity, academic achievement, or staying in school.

We use two waves of data from the National Longitudinal Survey of Adolescent Health (Add Health) and employ both regression models and INDIRECT, a syntax file that allows for the testing of indirect effects using SPSS (Preacher & Hayes, 2008).

We find that the long-term association between childhood neglect and violence in the transition to adulthood is robust in models controlling for GPA, physical abuse, and other forms of offending. Neglect did not have an indirect effect on violence through attachment, negative emotionality, or academic achievement but did have indirect effects on violence through its association with staying in school and with alcohol use.

This set of analyses was exploratory in nature. Further research on neglect should be undertaken, using finely tuned measures and research questions. In addition, our findings imply that the association between neglect and later violent behavior may be intertwined with certain dynamics of physical abuse and alcohol use, which should be further studied.